Knowledge that Transforms

Low engagement rates and high attrition rates have been formidable challenges to mobile apps and their long-term success. To date, little is known about how companies can scientifically detect user engagement stages and optimize corresponding personalized-targeting promotion strategies to improve business revenues. This paper proposes a new structural forward-looking hidden Markov model as combined with a randomized field experiment on app notification promotions. Our model can recover consumer latent engagement stages by accounting for both the time-varying nature of users’ engagement and their forward-looking consumption behavior. The structural estimates from the FHMM with the field-experimental data enable us to identify heterogeneity in the treatment effects. Additionally, we simulate and optimize the revenues of different personalized-targeting promotion strategies with the structural estimates. Personalized dynamic engagement-based targeting based on the FHMM can generate substantially higher revenues than the experience-based targeting strategy applied by current industry practices and targeting strategies based on alternative customer segmentation models. Overall, the novel feature of our paper is its proposal of a new personalized-targeting approach combining the FHMM with a field experiment to tackle the challenge of low engagement with mobile apps.

Online retailers often face the decision on whether they should implement a word-of-mouth (WOM) system on their websites. Some retailers opt not to have an in-site WOM system (e.g., Tiffany.com and Tjmaxx.com ), whereas others implement and manage an internal WOM system (e.g., Amazon.com and Macys.com ). According to a new study in Information Systems Research, authors Ni Huang (Arizona State University), Tianshu Sun (University of Southern California), Peiyu Chen (Arizona State University), and Joseph M. Golden ( Collage.com ) conducted a randomized field experiment to examine how implementing an in-site WOM system from ground zero influences customer conversion in an online retailing website. Their results demonstrate that implementing an in-site WOM system can be a double-edged sword to the online retailing website. Specifically, the impact of a WOM system implementation on customer conversion is moderated by WOM volume, such that its effect is positive above a threshold of volume and negative below the threshold. Additionally, the researchers find that WOM valence reinforces the impact of a WOM system on customer conversion. These results offer important implications for the hundreds-of-billion-dollar online retailing industry on the implementation and management of online WOM systems.

The 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act is landmark legislation that places electronic health record (EHR) technologies at the center of health system reform in the United States. This study leverages the meaningful use (MU) provisions of the HITECH Act to quantify different degrees of EHR use in a large and heterogeneous set of hospitals and investigates the impact of EHR use on quality of care. The results provide evidence of EHRs’ positive quality effects and reconcile earlier mixed findings in the EHR evaluation literature by showing that their benefits vary according to different levels of use and hospital characteristics. The effect sizes were larger in disadvantaged (i.e., small and rural) hospitals, suggesting the potential of EHRs in mitigating the disparities in the quality of healthcare.

A prime example of an emerging two-sided market is the driverless vehicle industry, an industry that will get much of its software from one side of the market: specifically, application developers. Consumers stand at the other side of this market. To what extent will this marketplace reward both the industry itself and application developers for technological innovation? In modeling this first question and keeping in mind consumers’ appetite for technological advances, we provide nuanced answers for executives in the driverless industry, application development firms, and government. This question speaks directly to what level of investment is optimal. Given that high government officials want to encourage the future growth of this vibrant industry, a second key theme of the paper is as follows. Should governments subsidize the focal industry or developers based on the extent to which they are innovative? Or should governments subsidize consumers? Our models conclude that subsidizing the industry is the overall best strategy followed by subsidies for consumers under certain other conditions. We find that it is not in the interest of society to subsidize application developers. Executives can use our models and results to fine tune to match their own circumstances with basic changes in our parameters.

How to improve the predictive accuracy of box office revenue with social media data is a big challenge and is particularly important for movie distributors and cinema operators. In this research, we find that microblogging UGC (MUGC) is a significant predictor of box office revenue and has stronger predictive power than UGC on Douban! Movies (DUGC) based on our examination of 60 movies released in China in 2012. To increase the attendance rate of movies, cinema operators can consider previous valence and volume of MUGC before scheduling the current film screenings because these messages can quickly predict the future box office revenue of a movie. Besides, we find that the volume of enterprise microblogs (i.e., MGC) can predict both box office revenue and MUGC, indicating that movie distributors should optimize their online media strategy by shifting more resources to utilizing enterprise microblogging. Although rebroadcasting volume from microblogging platforms does not predict box office revenue directly, it can indirectly predict it via MGC. Accordingly, compared with third-party platforms, rebroadcasting as one of the key distinct functions of microblogging platforms also shows its usefulness in box office revenue prediction. Overall, metrics from microblogging platforms are more effective in predicting box office revenue than those from third-party platforms.

The sale of virtual currency has become an important revenue source in the digital gaming industry. This paper analyzes the impact of this business model on players’ gaming behavior, the game provider’s strategies for virtual currency price and ad level, and social welfare. Our findings have important managerial implications for the gaming industry and policy makers. We find that when the enabling-power rate of virtual currency (i.e., how much players benefit from enhanced gameplay due to virtual currency generated per unit of playing time) is stronger, players have lower incentive to purchase virtual currency. Therefore, the provider should set a lower price to avoid losing too much demand. In the meantime, the marginal benefit of playing the game increases, and thus the provider should set a higher ad level to take advantage of this boosted marginal benefit for players when the base valuation of gameplay is sufficiently low. Finally, we demonstrate that offering in-game purchases of virtual currency as a new business model benefits society as a whole. Our findings suggest that regulators of the gaming industry should be less concerned about the risk of excessive gameplay for games that sell virtual currency compared with those that do not.

Digital infrastructures result from individual yet interdependent systems evolving in relation to each other. This paper identifies three processes by which individual systems become embedded into digital infrastructures. The first is a parallel process, whereby systems become embedded independently of each other. The second is a competitive process, whereby systems compete for resources and attention and one system usually thrives while the other system loses importance. The third is a spanning process characterizing a situation of boundary-spanning between distinct parts of a digital infrastructure. The three processes, synthesized into a dynamic model of digital infrastructure embeddedness, offer clarity to the question how digital infrastructures evolve. They also provide insight into the emergence of three forms of digital infrastructures: silofied, regenerated, and unified. Reflecting an interconnection view, our research further facilitates an understanding of infrastructure inertia and its associated consequence. Criticality traps should be avoided by considering the right timing for system replacement in the light of growing embeddedness over time.

Healthcare portals are gaining in popularity, connecting doctors with potential consumers of healthcare services. As online search and transaction marketplaces, they bring both sides of the market onto the same platform. Managers or platform owners seek to create value by increasing the number of users on either side of demand and supply of services. User-generated activity on Q&A forums of such sites reduces information asymmetry and indicates an increased adoption by either side. In this study, we have provided insights into understanding drivers for increased recommendations for doctors in online healthcare-services marketplace. The identification of these drivers and their directionality, interplay, and magnitude of impact are all of direct relevance to site promoters and managers as well as users. We find that the introduction of doctors’ responses has a significant causal impact on demand-side user perception of medical services offered. More importantly, our research suggests that doctors’ specialty, experience, qualifications, transparency in appointment booking, service fees, and response quality moderate the effect of doctors’ Q&A responses on user recommendations.